Description

After exploring the general pattern of modelling GPP vs observational GPP, the next step to identify the specific period when the mismatch between modeled GPP and observed GPP in each site–>focused in the markdown file

step1: tidy the table for GPP simulation vs GPP obs sites

step2: finding the way to separate out the model early simulation period

step1: tidy the table

library(kableExtra)
library("readxl")
table.path<-"D:/data/photocold_project/sel_sites_info/Using_sites_in_Fluxnet2015/"
load(file=paste0(table.path,"df_sites_avai.RDA"))
my_data<-df_sites_avai
my_data %>%
  kbl(caption = "Summary of sites with GPP estimation ") %>%
  kable_classic(full_width = F, html_font = "Cambria")
Summary of sites with GPP estimation
sitename lon lat elv year_start year_end classid c4 whc koeppen_code igbp_land_use plant_functional_type
BE-Bra 4.5206 51.3092 16 1996 2014 MF FALSE 85.68380 Cfb Mixed Forests Deciduous Broadleaf Trees
BE-Vie 5.9981 50.3051 493 1996 2014 MF FALSE 312.76520 Cfb Mixed Forests Deciduous Broadleaf Trees
CA-Man -98.4808 55.8796 259 1994 2008 ENF FALSE 52.67784 Dfc Evergreen Needleleaf Forest Evergreen Needleleaf Trees
CA-NS1 -98.4839 55.8792 260 2001 2005 ENF FALSE 50.25988 Dfc Evergreen Needleleaf Forest Evergreen Needleleaf Trees
CA-NS2 -98.5247 55.9058 260 2001 2005 ENF FALSE 59.02733 Dfc Evergreen Needleleaf Forest Evergreen Needleleaf Trees
CA-NS3 -98.3822 55.9117 260 2001 2005 ENF FALSE 115.96288 Dfc Evergreen Needleleaf Forest Evergreen Needleleaf Trees
CA-NS4 -98.3822 55.9117 260 2002 2005 ENF FALSE 115.96288 Dfc Evergreen Needleleaf Forest Evergreen Needleleaf Trees
CA-NS5 -98.4850 55.8631 260 2001 2005 ENF FALSE 32.74040 Dfc Evergreen Needleleaf Forest Evergreen Needleleaf Trees
CA-Qfo -74.3421 49.6925 382 2003 2010 ENF FALSE 176.82556 Dfc Evergreen Needleleaf Forest Evergreen Needleleaf Trees
CA-SF1 -105.8176 54.4850 536 2003 2006 ENF FALSE 265.99557 Dfc Evergreen Needleleaf Forest Evergreen Needleleaf Trees
CA-SF2 -105.8775 54.2539 520 2001 2005 ENF FALSE 286.65930 Dfc Mixed Forests Evergreen Needleleaf Trees
CH-Lae 8.3650 47.4781 689 2004 2014 MF FALSE 292.45551 Cfb Mixed Forests Deciduous Broadleaf Trees
CN-Qia 115.0581 26.7414 64 2003 2005 ENF FALSE 303.67596 Cfa Woody Savannas Shrub
CZ-BK1 18.5369 49.5021 875 2004 2008 ENF FALSE 260.95676 Dfb Evergreen Needleleaf Forest Evergreen Needleleaf Trees
DE-Hai 10.4530 51.0792 430 2000 2012 DBF FALSE 282.66736 Cfb Mixed Forests Deciduous Broadleaf Trees
DE-Lkb 13.3047 49.0996 1308 2009 2013 ENF FALSE 189.99904 Cfb Evergreen Needleleaf Forest Evergreen Needleleaf Trees
DE-Obe 13.7196 50.7836 735 2008 2014 ENF FALSE 246.86536 Cfb Evergreen Needleleaf Forest Evergreen Needleleaf Trees
DE-Tha 13.5669 50.9636 380 1996 2014 ENF FALSE 295.66315 Cfb Evergreen Needleleaf Forest Evergreen Needleleaf Trees
DK-Sor 11.6446 55.4859 40 1996 2014 DBF FALSE 226.43781 Cfb Deciduous Broadleaf Forest Deciduous Broadleaf Trees
FI-Hyy 24.2950 61.8475 181 1996 2014 ENF FALSE 255.05896 Dfc Evergreen Needleleaf Forest Evergreen Needleleaf Trees
FR-Fon 2.7801 48.4764 103 2005 2014 DBF FALSE 335.19290 Cfb Deciduous Broadleaf Forest Deciduous Broadleaf Trees
FR-LBr -0.7693 44.7171 61 1996 2008 ENF FALSE 269.57657 Cfb Cropland/Natural Vegetation Mosaic Shrub
IT-Col 13.5881 41.8494 1560 1996 2014 DBF FALSE 267.97675 Cfa Deciduous Broadleaf Forest Deciduous Broadleaf Trees
IT-Isp 8.6336 45.8126 210 2013 2014 DBF FALSE 320.68103 Cfb Woody Savannas Deciduous Broadleaf Trees
IT-La2 11.2853 45.9542 1350 2000 2002 ENF FALSE 237.59509 Cfb Evergreen Needleleaf Forest Evergreen Needleleaf Trees
IT-Lav 11.2813 45.9562 1353 2003 2014 ENF FALSE 249.79709 Cfb Evergreen Needleleaf Forest Evergreen Needleleaf Trees
IT-PT1 9.0610 45.2009 60 2002 2004 DBF FALSE 317.98535 Cfa Croplands Cereal crop
IT-Ren 11.4337 46.5869 1730 1998 2013 ENF FALSE 167.45172 Dfc Evergreen Needleleaf Forest Evergreen Needleleaf Trees
JP-MBF 142.3186 44.3869 545 2003 2005 DBF FALSE 214.18483 Dfb Mixed Forests Deciduous Broadleaf Trees
JP-SMF 137.0788 35.2617 175 2002 2006 MF FALSE 294.94739 Cfa Croplands Cereal crop
NL-Loo 5.7436 52.1666 25 1996 2013 ENF FALSE 71.05942 Cfb Evergreen Needleleaf Forest Evergreen Needleleaf Trees
RU-Fyo 32.9221 56.4615 265 1998 2014 ENF FALSE 301.45709 Dfb Mixed Forests Evergreen Needleleaf Trees
US-GBT -106.2397 41.3658 3191 1999 2006 ENF FALSE 219.37785 Dfc Evergreen Needleleaf Forests (null)
US-GLE -106.2399 41.3665 3197 2004 2014 ENF FALSE 207.54053 Dfb Evergreen Needleleaf Forest Evergreen Needleleaf Trees
US-Ha1 -72.1715 42.5378 340 1991 2012 DBF FALSE 193.85033 Dfb Mixed Forests Deciduous Broadleaf Trees
US-MMS -86.4131 39.3232 275 1999 2014 DBF FALSE 343.01581 Cfa Deciduous Broadleaf Forest Deciduous Broadleaf Trees
US-NR1 -105.5464 40.0329 3050 1998 2014 ENF FALSE 170.75986 Dfc Evergreen Needleleaf Forest Evergreen Needleleaf Trees
US-PFa -90.2723 45.9459 470 1995 2014 MF FALSE 203.73933 Dfb Mixed Forests Deciduous Broadleaf Trees
US-Prr -147.4876 65.1237 210 2010 2013 ENF FALSE 382.20374 Dfc Evergreen Needleleaf Forests Evergreen Needleleaf Trees
US-Syv -89.3477 46.2420 540 2001 2014 MF FALSE 222.69208 Dfb Mixed Forests Deciduous Broadleaf Trees
US-UMB -84.7138 45.5598 234 2000 2014 DBF FALSE 174.07025 Dfb Deciduous Broadleaf Forest Deciduous Broadleaf Trees
US-UMd -84.6975 45.5625 239 2007 2014 DBF FALSE 235.31183 Dfb Mixed Forests Deciduous Broadleaf Trees
US-WCr -90.0799 45.8059 520 1999 2014 DBF FALSE 264.96152 Dfb Deciduous Broadleaf Forest Deciduous Broadleaf Trees
US-Wi0 -91.0814 46.6188 349 2002 2002 ENF FALSE 325.71179 Dfb Mixed Forests Evergreen Needleleaf Trees
US-Wi3 -91.0987 46.6347 411 2002 2004 DBF FALSE 343.67532 Dfb Deciduous Broadleaf Forest Deciduous Broadleaf Trees
US-Wi4 -91.1663 46.7393 352 2002 2005 ENF FALSE 299.29538 Dfb Mixed Forests Evergreen Needleleaf Trees
US-Wi9 -91.0814 46.6188 350 2004 2005 ENF FALSE 325.71179 Dfb Mixed Forests Evergreen Needleleaf Trees

step2: seprate the time period when model early estimation of GPP

Part1: find the method to determine the period that with early GPP estimation

Part 2: check all the sites (47 sites–>used 39 sites at the end)

(1) For Cfa:both for DBF, MF, and ENF sites(5 sites–>used 5 sites at the end)

my_data_Cfa<-my_data[my_data$koeppen_code=="Cfa",]
  • Cfa-DBF (3 sites)
## [1] 9

## [1] 2

## [1] 15

- Cfa-MF (1 site)

## [1] 4

- Cfa-ENF (1 site)

## [1] 3

(2) For Cfb: for DBF,MF and ENF (14 sites–>used 12 sites at the end)

my_data_Cfb<-my_data[my_data$koeppen_code=="Cfb",]
  • Cfb-DBF (4 sites)
## [1] 13

## [1] 13

## [1] 8

## [1] 2

- Cfb-MF (3 sites–>2 sites used at the end)

## [1] 15

## [1] 10

- Cfb-ENF (7 sites–>6 sites used at the end)

## [1] 4

## [1] 7

## [1] 15

## [1] 6

## [1] 12

## [1] 14

(3) For Cfc: 0 sites

my_data_Cfc<-my_data[my_data$koeppen_code=="Cfc",]

(4) For Dfa: 0 sites 

my_data_Dfa<-my_data[my_data$koeppen_code=="Dfa",]

(5) For Dfb: for DBF, MF and ENF (14 sites–>used 10 sites in the end) 

my_data_Dfb<-my_data[my_data$koeppen_code=="Dfb",]
  • Dfb-DBF (6 site–>used 5 sites in the end)
## [1] 2

## [1] 15

## [1] 7

## [1] 13

## [1] 11

- Dfb-MF (2 sites–>used 2 sites)

## [1] 14

## [1] 6

- Dfb-ENF (6 sites–>used 3 sites in the end)

## [1] 5

## [1] 14

## [1] 9

(6) For Dfc:for ENF sites (14 sites–> used 12 sites at the end)

my_data_Dfc<-my_data[my_data$koeppen_code=="Dfc",]
  • Dfc-ENF (14 sites–>at the end used 12 sites)
## [1] 6

## [1] 3

## [1] 3

## [1] 4

## [1] 2

## [1] 4

## [1] 7

## [1] 15

## [1] 11

## [1] 2

## [1] 15

## [1] 2

(7) For Dfd:for ENF sites(0 sites)

my_data_Dfd<-my_data[my_data$koeppen_code=="Dfd",]

##updates: 11-19 ## step3: save the data that label with “is_event”

Summary

steps to determine the “is_event” period

**Step1: normlization for all the years in one site**
 
#normalized the gpp_obs and gpp_mod using the gpp_max(95 percentile of gpp)


**Step 2:Determine the green-up period for each year(using spline smoothed values):** 

#followed analysis is based on the normlized "GPP_mod"time series(determine earlier sos)

- using the normalized GPP_mod to determine sos,eos and peak of the time series (using the threshold, percentile 10 of amplitude, to determine the sos and eos in this study). We selected the GPP_mod to determine the phenophases as genearlly we can get earlier sos compared to GPP_obs--> we can have larger analysis period
- update in Aug,31,2011-->limit the sos late than Feburary(Doy:60)-->in order to remove some unrelastic sos

**Step 3:rolling mean of GPPobs and GPPmod for data for all the years(moving windown:5,7,10, 15, 20days)**

**also for the data beyond green-up period--> the code of this steps moves to second step**

- at the end, I select the 20 days windows for the rolling mean


**Step 4:Fit the Guassian norm distribution for residuals beyond the green-up period**

- The reason to conduct this are: we assume in general the P-model assume the GPP well outside the green-up period (compared to the observation data). 

- But in practise, the model performance is not always good beyond the green-up period-->I tested three data range:

  a. [peak,265/366]
  
  b. DoY[1, sos]& DOY[peak,365/366]
  
  c. [1,sos] & [eos,365/366]
  
I found the using the data range c, the distrbution of biase (GPP_mod - GPP_obs) is more close to the norm distribution, hence at end of I used the data range c to build the distribution.


**step 5:determine the "is_event" within green-up period**

  - After some time of consideration, I took following crition to determine the "is_event":
  
    1) during the green-up period (sos,peak)-->the data with GPP biases bigger than 3 SD are classified as the "GPP overestimation points"
    2) For "GPP overestimation points" --> only regard the data points in the first 2/3 green-up period as the "is_event"
    
    3) For "is_event points", thoses are air temparture is less than 10 degrees will be classified as the "is_event_less10". I selected 10 degree as the crition by referring to the paper Duffy et al., 2021 and many papers which demonstrate the temperature response curve normally from 10 degree (for instance: Lin et al., 2012)
  
    References:
    
    Duffy et al., 2021:https://advances.sciencemag.org/content/7/3/eaay1052
    
    Lin et al., 2012:https://academic.oup.com/treephys/article/32/2/219/1657108
    
  
**step 6:Evaluation "is_event"-->visualization and stats**

 - two ways to evaluate if "is_event" is properly determined:
 
  1) visulization
  
  2) stats:
  $$
  Pfalse = /frac{days(real_{(is-event)})}{days(flagged_{(is-event)})} 
  $$